معرفی
Dr. Adrian Lison is a researcher at ETH Zürich's Computational Evolution Group in Basel, Switzerland. He specializes in developing statistical methods for infectious disease surveillance with a particular focus on wastewater-based epidemiology and real-time transmission dynamics tracking.
- Current position: Researcher at Computational Evolution Group, ETH Zürich
- Location: BSS J 6.1, Klingelbergstrasse 48, 4056 Basel, Switzerland
- ORCID: 0000-0002-6822-8437
Dr. Lison's research focuses on developing statistical methods for infectious disease surveillance, particularly for real-time tracking of transmission dynamics using mobility data, nowcasting, and wastewater-based epidemiology. His day-to-day work involves hierarchical, semi-mechanistic Bayesian modeling in Stan and open source R package development. He is the author of the wastewater modeling tool EpiSewer and coauthor of the epinowcast epidemiological modeling tool. His methodological work centers on Bayesian statistical approaches to extract meaningful epidemiological parameters from complex data sources, with particular emphasis on addressing the challenges of wastewater concentration measurements.
Dr. Lison's publications demonstrate a strong focus on methodological innovation in epidemiological modeling, particularly in adapting Bayesian statistical approaches to wastewater-based surveillance systems. His EpiSewer package provides comprehensive tools for estimating effective reproduction numbers and other epidemiological parameters from wastewater concentration measurements, offering features like non-daily measurement handling, flow normalization, and sewer residence time distribution modeling.
- Beta Gamma Sigma (2019)
- German Academic Scholarship Foundation (2015)
- National Winner, 32. Bundeswettbewerb Informatik (2014)
Dr. Lison's work involves significant computational research and software development, with a focus on creating open-source tools that advance epidemiological surveillance capabilities. His EpiSewer package has gained recognition in the field, with 22 GitHub stars and multiple citations. His research bridges the gap between theoretical statistical methodology and practical public health applications, particularly in the context of infectious disease monitoring.
